The accurate non-invasive staging of liver fibrosis using deep learning radiomics based on transfer learning of shear wave elastography

Zhou, Hui; Wang, Kun; Tian, Jie

doi:10.1117/12.2549425

Cited by 2 publications

(8 citation statements)

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“…Studies were published between 2017 and 2020. Imaging modalities included US [21][22][23][24][25][26][27][28][29][30] (n = 10, 63%), MRI [31][32][33] (n = 3, 19%), and computed tomography (CT) [34][35][36] (n = 3, 19%). A total of 40 405 radiological images were analyzed from 15 853 patients.…”

Section: Study Selection and Characteristicsmentioning

confidence: 99%

“…Ten studies [21][22][23][24][25][26][27][28][29][30] classified fibrosis using US images. Six focused on shear wave elastography (SWE), 21,22,[24][25][26]29 one on transient elastography (TE), 7 and three on non-elastography US images. 23,28,30 SWE-US and TE studies showed consistent and promising results for predicting liver fibrosis.…”

Section: Us Imaging Classificationmentioning

confidence: 99%

“…Six-teen studies [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36] met our inclusion criteria (Figure 2). Studies were published between 2017 and 2020.…”

Section: Study Selection and Characteristicsmentioning

confidence: 99%

“…Table 1 summarizes the characteristics of the included studies, and Table 2 presents the patients inclusion and exclusion criteria. All but two studies 22,29 were retrospective.…”

Section: Study Selection and Characteristicsmentioning

confidence: 99%

Section: Us Imaging Classificationmentioning

confidence: 99%

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Deep learning for noninvasive liver fibrosis classification: A systematic review

Anteby

Klang

Horesh

et al. 2021

Liver International

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Background and Aims While biopsy is the gold standard for liver fibrosis staging, it poses significant risks. Noninvasive assessment of liver fibrosis is a growing field. Recently, deep learning (DL) technology has revolutionized medical image analysis. This technology has the potential to enhance noninvasive fibrosis assessment. We systematically examined the application of DL in noninvasive liver fibrosis imaging. Methods Embase, MEDLINE, Web of Science, and IEEE Xplore databases were used to identify studies that reported on the accuracy of DL for classification of liver fibrosis on noninvasive imaging. The search keywords were “liver or hepatic,” “fibrosis or cirrhosis,” and “neural or DL networks.” Risk of bias and applicability were evaluated using the QUADAS‐2 tool. Results Sixteen studies were retrieved. Imaging modalities included ultrasound (n = 10), computed tomography (n = 3), and magnetic resonance imaging (n = 3). The studies analyzed a total of 40 405 radiological images from 15 853 patients. All but two of the studies were retrospective. In most studies the “ground truth” reference was the METAVIR score for pathological staging (n = 9.56%). The majority of the studies reported an accuracy >85% when compared to histopathology. Fourteen studies (87.5%) had a high risk of bias and concerns regarding applicability. Conclusions Deep learning has the potential to play an emerging role in liver fibrosis classification. Yet, it is still limited by a relatively small number of retrospective studies. Clinicians should facilitate the use of this technology by sharing databases and standardized reports. This may optimize the noninvasive evaluation of liver fibrosis on a large scale.

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Section: Study Selection and Characteristicsmentioning

confidence: 99%

Section: Us Imaging Classificationmentioning

confidence: 99%

“…Six-teen studies [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36] met our inclusion criteria (Figure 2). Studies were published between 2017 and 2020.…”

Section: Study Selection and Characteristicsmentioning

confidence: 99%

“…Table 1 summarizes the characteristics of the included studies, and Table 2 presents the patients inclusion and exclusion criteria. All but two studies 22,29 were retrospective.…”

Section: Study Selection and Characteristicsmentioning

confidence: 99%

Section: Us Imaging Classificationmentioning

confidence: 99%

See 3 more Smart Citations

Deep learning for noninvasive liver fibrosis classification: A systematic review

Anteby

Klang

Horesh

et al. 2021

Liver International

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Application and prospects of AI-based radiomics in ultrasound diagnosis

Zhang,

Meng,

et al. 2023

Vis. Comput. Ind. Biomed. Art

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Artificial intelligence (AI)-based radiomics has attracted considerable research attention in the field of medical imaging, including ultrasound diagnosis. Ultrasound imaging has unique advantages such as high temporal resolution, low cost, and no radiation exposure. This renders it a preferred imaging modality for several clinical scenarios. This review includes a detailed introduction to imaging modalities, including Brightness-mode ultrasound, color Doppler flow imaging, ultrasound elastography, contrast-enhanced ultrasound, and multi-modal fusion analysis. It provides an overview of the current status and prospects of AI-based radiomics in ultrasound diagnosis, highlighting the application of AI-based radiomics to static ultrasound images, dynamic ultrasound videos, and multi-modal ultrasound fusion analysis.

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The accurate non-invasive staging of liver fibrosis using deep learning radiomics based on transfer learning of shear wave elastography

Cited by 2 publications

References 0 publications

Deep learning for noninvasive liver fibrosis classification: A systematic review

Deep learning for noninvasive liver fibrosis classification: A systematic review

Application and prospects of AI-based radiomics in ultrasound diagnosis

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